In this paper, we show that communication can be sufficient to sustain a world model, making it possible to exploit centralized training further by training policies without interacting with the environment. Furthermore, it is sufficient to only communicate discrete messages with the neighbouring agents to update a world model during execution phase. We denote our approach as MAMBA, which is derived from Multi-Agent Model-Based Approach. To our knowledge, this is the first method that extends world models to environments with an arbitrary number of agents and that views world models as an instance of communication. We show that our method can reduce the required number of samples by an order of magnitude compared to MFRL approaches and consistently outperform current state-of-the-art methods in low data regime in challenging environments of SMAC [1]} and Flatland [2]}.
